Application of the subspace method to speech recognition

In this paper, the subspace method of pattern recognition, in which method classification is decided by the largest projection of an unknown pattern vector onto subspaces corresponding to different classes, is applied to the recognition of continuous Finnish speech. Classification is based on phonemic power spectra produced by an analog filter bank. When compared, e.g., with the nearest-neighbor method and the method of direction cosines, the advantages of the subspace method are an improved stability of classification and a more balanced total classification accuracy of the different phonemic classes with respect to their relative frequencies of occurrence. The efficient spanning of the subspaces as well as their mutual orthogonalization are discussed. Furthermore, the close relationship between the phonemic labeling and segmentation when using the subspace method is pointed out.